Received Signal Strength Indicator Values Research Articles

Research of localization algorithm based on weighted Voronoi diagrams for wireless sensor network

Wireless sensor network (WSN) is formed by a large number of cheap sensors, which are communicated by an ad hoc wireless network to collect information of sensed objects of a certain area. The acquired information is useful only when the locations of sensors and objects are known. Therefore, localization is one of the most important technologies of WSN. In this paper, weighted Voronoi diagram-based localization scheme (W-VBLS) is proposed to extend Voronoi diagram-based localization scheme (VBLS). In this scheme, firstly, a node estimates the distances according to the strength of its received signal strength indicator (RSSI) from neighbor beacons and divides three beacons into groups, whose distances are similar. Secondly, by a triangle, formed by the node and two beacons of a group, a weighted bisector can be calculated out. Thirdly, an estimated position of the node with the biggest RSSI value as weight can be calculated out by three bisectors of the same group. Finally, the position of the node is calculated out by the weighted average of all estimated positions. The simulation shows that compared with centroid and VBLS, W-VBLS has higher positioning accuracy and lower computation complexity.

Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network

Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID) indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF) neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.

RSSI기반 지능형 위치 추정 시스템 설계 및 구현

본 논문에서는 모바일 로봇이 주어진 환경에서 물건 객체를 찾기 위해 RFID(Radio Frequency IDentification) 태그의 위치를 추정할 수 있는 지능형 시스템을 설계하고 구현하였다. 개발된 지능형 시스템은 인공 신경망 시스템으로 RSSI(Received Signal Strength Indication)값을 입력으로 하고 절대 위치 좌표 값을 목표 값으로 하는 학습 시스템이다. 위치 추정을 위하여 수동형 RFID를 사용하였으며 능동형으로의 확장도 고려하였다. 실내에서 위치 추정을 위한 환경을 구축하여 사용될 수 있도록 시스템을 설계하였다. 또한 설계된 시스템을 소프트웨어 개발을 하였고, 실험을 통해 테스트베드 상에서 시스템 학습과 관련된 결과를 보여준다. 실제 현장과 유사한 환경에서 학습데이터 생성에서부터 실시간 위치 추정과 관련된 다양한 실험 결과를 보여준다. 실험 결과를 통해 모바일 로봇이 실내에서 원하는 객체를 쉽게 찾을 수 있음을 확인하였다. In this paper, we design and implement an intelligent system for finding objects with RFID(Radio Frequency IDentification) tag in which an mobile robot can do. The system we developed is a learning system of artificial neural network that uses RSSI(Received Signal Strength Indicator) value as input and absolute coordination value as target. Although a passive RFID is used for location estimation, we consider an active RFID for expansion of recognition distance. We design the proposed system and construct the environment for indoor location estimation. The designed system is implemented with software and the result related learning is shown at test bed. We show various experiment results with similar environment of real one from earning data generation to real time location estimation. The accuracy of location estimation is verified by simulating the proposed method with allowable error. We prepare local test bed for indoor experiments and build a mobile robot that can find the objects user want.

Power Balance AODV Algorithm of WSN in Agriculture Monitoring

WSN (wireless sensor networks) is a kind of energy-constrained network, which has been widely used in precision agriculture environment monitoring. However, power balance is not taken into account in traditional routing algorithms. In this paper, a novel Power Balance Ad hoc On-Demand Distance Vector (PB-AODV) routing algorithm is proposed on cross-layer design to solve the problem of power balance. The main idea of our proposed algorithm is that, routing path is established by the received signal strength indication (RSSI) value in the route discovery process of PB-AODV. The optimal transmitting power which can be computed by RSSI value, power threshold and node’s surplus energy is encapsulated into route reply packet (RRP). Thus, the sender node can adaptively adjust its transmission power to save energy with the RRP. Simulation results show that the proposed algorithm is effective for load balancing, and increases the WSN’s lifetime about 14.3%.

Optimization of an RFID location identification scheme based on the neural network

SummaryAn indoor localization technology is increasingly critical as location‐aware applications evolve. Researchers have proposed several indoor localization technologies. Because most of the proposed indoor localization technologies simply involve using the received signal strength indicator value of radio‐frequency identification (RFID) for indoor localization, radio‐frequency interference, and environmental factors often limit the accuracy of localization results. Therefore, this study proposes an accurate RFID localization based on the neural network (ARL‐N2), a passive RFID indoor localization scheme for identifying tag positions in a room, combining a location identification based on dynamic active RFID calibration algorithm with a backpropagation neural network (BPN). The proposed scheme composed of two phases: in the training phase, an appropriate BPN architecture is constructed using the training data derived from the coordinates of reference tags and the coordinates obtained using the localization algorithm. By contrast, the online phase involves calculating the tracking tag coordinates and using these values as BPN inputs, thereby enhancing the estimated location. A performance evaluation of the ARL‐N2 schemes confirms its high localization accuracy. The proposed method can be used to locate critical objects in difficult‐to‐find areas by creating minimal errors and applying and economical technique. Copyright © 2013 John Wiley & Sons, Ltd.

Wireless ESD Event Locator Systems in Hard Disk Drive Manufacturing Environments

This paper presents two wireless systems, namely, an electromagnetic interference (EMI)-strength-based system and a received signal strength indicator (RSSI)-based system, that locate electrostatic discharge (ESD) events in hard disk drive (HDD) manufacturing environments. The EMI-strength-based system is composed of four EMI detectors and estimates an ESD event position using an enhanced trilateration method. The RSSI-based system consists of three stationary wireless sensor nodes and one mobile wireless sensor node. The stationary nodes provide reference positions, whereas the mobile node is equipped with an EMI detector, which is used to detect an ESD event throughout the manufacturing line. Subsequently, the ESD event position is estimated by an RSSI-based localization method. In this RSSI-based system, a new method is also introduced to compensate the attenuated RSSI values by using a neural network and various transmission powers. Both systems were evaluated in a real HDD setting, and their advantages and disadvantages are discussed here. The evaluation results indicate that both systems worked well. However, the RSSI-based system can support a larger area compared with the EMI-strength-based system.

An Experimental Analysis of Effects of IEEE 802.11 Channels on RSSI-based Indoor Localization System with IEEE 802.15.4

Many indoor localization studies are now actively underway. Generally, the indoor localization algorithms are based on the IEEE 802.15.4 wireless sensor networks. However, because of the sensor nodes’ limitations, their radios are more susceptible to noise and interference than other wireless technologies. These are critical factors in the rigorous indoor environment, whose effects may not be the same on all channels of a sensor network. Many buildings are equipped with IEEE 802.11 access point (AP) to provide internet service. IEEE 802.11 AP can have a serious impact on the IEEE 802.15.4 performance if the channel allocation is not carefully taken into account. In this paper, we analyze the effect of IEEE 802.11 AP on location errors in received signal strength indicator (RSSI)-based indoor localization system with IEEE 802.15.4. We performed several tests in a house in order to minimize the influence of other IEEE 802.11 APs. We performed transmitting/receiving test by varying the location of IEEE 802.11 AP. Also, we composed an indoor localization system, and measured the location error of mobile node by varying the location of IEEE 802.11 AP. Our study shows that co-channel interference due to use IEEE 802.11 AP affects the RSSI value of sensor node. Ultimately, it has a harmful influence on location error.

DuRT: Dual RSSI Trend Based Localization for Wireless Sensor Networks

Localization is a key issue in wireless sensor networks. The geographical location of sensors is important information that is required in sensor network operations such as target detection, monitoring, and rescue. These methods are classified into two categories, namely range-based and range-free. Range-based localizations achieve high location accuracy by using specific hardware or using absolute received signal strength indicator (RSSI) values, whereas range-free approaches obtain location estimates with lower accuracy. Because of the hardware and energy constraints in sensor networks, RSSI offers a convenient method to find the position of sensor nodes. However, in the presence of channel noise, fading, and attenuation, it is not possible to estimate the actual location. In this paper, we propose an RSSI-based localization scheme that considers the trend of RSSI values obtained from beacons to estimate the position of sensor nodes. Through applying polynomial modeling on the relationship between received RSSI and distance, we are able to locate the maximum RSSI point on the anchor trajectory. Using two such trajectories, the sensor position can be determined by calculating the intersection point of perpendiculars passing through the maximum RSSI point on each trajectory. In addition, we devised schemes to improve the localization method to perform under a variety of cases such as single trajectory, unavailability of RSSI trends, and so. The advantage of our scheme is that it does not rely on absolute RSSI values and hence, can be applied in dynamic environments. In simulations, we demonstrate that the proposed localization scheme achieves higher location accuracy compared with existing localization approaches.

Indoor Localization Using FM Signals

The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi received signal strength indicator (RSSI) signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath, and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this paper, we demonstrate through a detailed experimental study in three different buildings across the US, that FM radio signal RSSI values can be used to achieve room-level indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath, etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5 percent. More importantly, we experimentally demonstrate that the localization errors of FM and WiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83 percent (when accounting for wireless signal temporal variations) compared to when WiFi RSSI only is used as a signature.

The short-term effects of antenna insulation thickness on path losses in wireless telemetry implants at microwave frequencies

Various physiological parameters can be monitored non-invasively using wireless biotelemetry links. The development of sophisticated ultra low power consuming transceivers allows the transmission of large amounts of data from the inside of the body to an external receiver in real time at microwave frequencies.Antenna impedance matching is crucial for obtaining an acceptable propagation link budget in a wireless telemetry link. The dielectric properties of biological tissue induce detuning to transceiver antennas when implanted into the body. To counteract detuning problems, implant antennas are coated with biocompatible insulating material. The study investigates the propagation losses of a wireless communication link at different insulation thicknesses of medical grade silicone in the Industrial-Scientific-Medical (ISM) radio band at 2.45 GHz. The wireless link consisted of an implantable unit which was placed between two pads of tissue substitute material and an external receiver which was connected to a laptop. Predefined data packets were transmitted from the implant, the received packets were analyzed, packet errors and packet losses were logged and the received signal strength indicator values (RSSI) were recorded. Our results showed that the mean RSSI values of insulated transmitter antennas - embedded in tissue equivalent material - provide more safety distance to critical receiver sensitivity level than uncoated antennas.The conducted measurements let us conclude that with increasing thickness of the insulation layer, the antenna becomes less sensitive to detuning by adjacent tissue substitute material. Therefore tuned antennas are less influenced by the surrounding tissue after implantation.

The short-term effects of antenna insulation thickness on path losses in wireless telemetry implants at microwave frequencies

Various physiological parameters can be monitored non-invasively using wireless biotelemetry links. The development of sophisticated ultra low power consuming transceivers allows the transmission of large amounts of data from the inside of the body to an external receiver in real time at microwave frequencies.Antenna impedance matching is crucial for obtaining an acceptable propagation link budget in a wireless telemetry link. The dielectric properties of biological tissue induce detuning to transceiver antennas when implanted into the body. To counteract detuning problems, implant antennas are coated with biocompatible insulating material. The study investigates the propagation losses of a wireless communication link at different insulation thicknesses of medical grade silicone in the Industrial-Scientific-Medical (ISM) radio band at 2.45 GHz. The wireless link consisted of an implantable unit which was placed between two pads of tissue substitute material and an external receiver which was connected to a laptop. Predefined data packets were transmitted from the implant, the received packets were analyzed, packet errors and packet losses were logged and the received signal strength indicator values (RSSI) were recorded. Our results showed that the mean RSSI values of insulated transmitter antennas - embedded in tissue equivalent material - provide more safety distance to critical receiver sensitivity level than uncoated antennas.The conducted measurements let us conclude that with increasing thickness of the insulation layer, the antenna becomes less sensitive to detuning by adjacent tissue substitute material. Therefore tuned antennas are less influenced by the surrounding tissue after implantation.

The application of an artificial immune system-based back-propagation neural network with feature selection to an RFID positioning system

This study uses the Received Signal Strength Indication (RSSI) values of RFID to predict the position of picking staff for warehouse management. A proposed feature selection-based back-propagation (BP) neural network that uses an artificial immune system (AIS) (FSBP-AIS) to determine the connecting weights of a neural network learns the relationship between the RSSI values and the position of the picking staff. In addition, the proposed FSBP-AIS is able to determine the representative features, or inputs, during training. Once a picking staff's position is known, this information is used to plan the picking route for picking staff if a new order arrives. The computational results indicate that the proposed FSBP-AIS can provide better predictions than a traditional BP neural network, BP neural network with stepwise regression to determine the important inputs, and regression method.

Localization with Single Stationary Anchor for Mobile Node in Wireless Sensor Networks

We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.

Topology control algorithm for wireless sensor network based on mean RSSI

With regard to the problem that Received Signal Strength Indicator(RSSI) has errors in spatial and temporal extension,a new distributed real wireless environment faced topology control algorithm named RTC was proposed in this paper.RTC used the mean RSSI value to compute the bi-directional path-loss and found a path with two hops between two nodes of which the link cost across each hop was less than that of these two nodes.The communication complexity and network connectivity of RTC were analyzed and simulation based analysis on energy property was presented.The simulation results show that RTC has such advantages as balanced energy consumption and long network lifetime.

An Empirical Interference Modeling for Link Reliability Assessment in Wireless Networks

In recent years, it has been widely believed in the community that the link reliability is strongly related to received signal strength indicator (RSSI) [or signal-to-interference-plus-noise ratio (SINR)] and external interference makes it unpredictable, which is different from the previous understanding that there is no tight relationship between the link reliability and RSSI (or SINR), but multipath fading causes the unpredictability. However, both cannot fully explain why the unpredictability appears in the link state. In this paper, we unravel the following questions: 1) What causes frame losses that are directly related to intermediate link states? 2) Is RSSI or SINR a right criterion to represent the link reliability? 3) Is there a better measure to assess the link reliability? We first configured a testbed for performing a real measurement study to identify the causes of frame losses, and observed that link reliability depends on an intraframe SINR distribution, not a single value of RSSI (or SINR). We also learned that an RSSI value is not always a good indicator to estimate the link state. We then conducted a further investigation on the intraframe SINR distribution and the relationship between the SINR and link reliability with the ns-2 simulator. Based on these results, we finally propose an interference modeling framework for estimating link states in the presence of wireless interferences. We envision that the framework can be used for developing link-aware protocols to achieve their optimal performance in a hostile wireless environment.

Efficient localization scheme with ring overlapping by utilizing mobile anchors in wireless sensor networks

This study proposes an efficient localization scheme in wireless sensor networks. The proposed scheme utilizes mobile anchors and is based on ring overlapping. In a wireless sensor network, the nodes that know their locations are called reference nodes, and the other nodes that are without the knowledge of their locations are called blind nodes. To localize a certain blind node, by comparing the relative RSSI (Received Signal Strength Indicator) values among nodes, mobile beacons are utilized to find out the rings that are centered at a reference node and contain the blind node. These rings are called B-Rings. Since the mobile anchors and the reference nodes know their own locations, the B-Rings can be precisely derived. Moreover, by using multiple mobile beacons, the widths of the B-Rings can be further minimized; and then by overlapping them, the location of the blind nodes can be efficiently estimated. Most existing localization schemes that utilize mobile anchors let the mobile anchors move randomly. In contrast, the proposed scheme provides regular and simple movement mechanisms for the mobile anchors. Thus, the mobile anchors consume less energy than the other schemes, in which the mobile anchors move randomly. Analytical analysis and simulation results show that the proposed localization mechanism can achieve better location accuracy as well as less movement length of the mobile anchor than the other existing related approaches.

Navigation algorithm for WSN mobile node on MH particle filtering improvement

A new approach is developed to localise and navigate Wireless Sensor Network (WSN) mobile node with Received Signal Strength Indicator (RSSI) signal, the approach is economical, convenient and reliable in noisy environment. An efficient Metropolis–Hasting (MH) particle filter algorithm was proposed to process the signal for ensures the monotonic increasing relationship between RSSI value and the distance between nodes. The coordinate space quantised with RSSI value was selected to describe the state and position of robots to avoid model error. The navigation system consists of several beacon nodes, and each of them is a distributed measurement and control unites. The outputs from every beacon nodes are collected by the navigation control centre, and the final outputs for mobile robots are calculated based on data fusion. Therefore, the real–time performance of this navigation system is enhanced. Furthermore, this system could adapt dynamic or unknown scenarios due to the coordinate of beacon node is not required before navigation. The simulation and experimental results show the effectiveness of this navigation algorithm.

Study on an Optimization Method of Deployment for Wireless Sensor Networks in Buildings

Wireless Sensor Networks (WSN) has been increasingly used in various fields of modern production and life, such as industrial control and environmental monitoring. However, there is no effective and universal algorithm at present for optimizing the deployment of WSN nodes in buildings. The main reason is the complex environment of the monitoring region which brings about the difficulty in determining the attenuation of wireless signal. The relationship between the Received Signal Strength Indicator (RSSI) and Packet Error Rate (PER) are discovered by the experimental method, then the corresponding mathematical model is also established by the experimental data. Based on different PER value, one method for optimizing the deployment of nodes in buildings is proposed by the RSSI value of the sensor node. This method can use fewer sensor nodes to cover the required monitoring region, which has the advantage of low-cost network construction, easy maneuverability and good versatility and et al. Finally, the deployment method of sensor nodes and the deployment example of temperature and humidity monitoring system are given in the paper.

Multi-target indoor localization and tracking on video monitoring system in a wireless sensor network

The development of wireless sensor networks (WSNs) has greatly encouraged the use of sensors for multi-target tracking. The high efficiency detection and location monitoring are critical requirements for multi-target tracking in a WSN. In this paper, we present an indoor tracking model using IEEE 802.15.4 compliant radio frequency and video monitoring system to monitor targets in a special way. Our motivation is to manipulate the erratic or unstable received signal strength indicator (RSSI) signals to deliver the stable and precise position information in the indoor environment. We propose a localization algorithm based on statistical uncorrelated vectors and develop a smoothing algorithm to minimize the noise in RSSI values. We also present a solution combining the WSN with the Ethernet technology to decrease the RSSI interference by buildings. The developed system can realize the functions of multi-target detection and tracking, and specific target inquiries, alarms and monitoring. The system architecture, hardware and software organization, as well as the solutions for multiple targets tracking, RSSI interference and localization accuracy have been introduced in details.

Monitor System to Detect Heat Stress and Position of Youth Lawn Care Workers

Due to inadequate training and an undeveloped ability to recognize dangerous scenarios, youth workers can be exposed to many dangers in the agriculture and lawn care industries. With the abundance of new technologies available on the market, a project was devised to reduce the risk of youth experiencing heat exhaustion and equipment run-overs by employing sensor based technologies. Using aural temperature measurement techniques involving a thermistor and thermopile, an accurate estimation of core body temperature was demonstrated. The measurements performed by the devices were recorded and transmitted wirelessly over a ZigBee network using XBee radiofrequency modules. Utilizing the properties of wireless transmission, the Received Signal Strength Indication (RSSI) was used to approximate the distance between devices. With accuracy comparable to GPS methods and no necessity for line of sight to sky, RSSI supplied an adequate estimate for proximity distance. The temperature and RSSI values were then sent to a coordinating modem where the data is displayed for the supervisor. After testing and calibrating the device, it was found that these methods are effective for the monitoring of core body temperature and proximity of workers. The temperature sensor was able to measure temperatures with less than 0.25% error and the proximity sensor was able to estimate distance within 1.25 m at close range.